backtracking sum of subsets and knapsack. backtracking 2 backtracking two versions of backtracking...

Backtracking

Sum of Subsets

and

Knapsack

Backtracking 2

Backtracking

• Two versions of backtracking algorithms– Solution needs only to be feasible (satisfy

problem’s constraints) • sum of subsets

– Solution needs also to be optimal– knapsack

Backtracking 3

The backtracking method• A given problem has a set of constraints and

possibly an objective function• The solution optimizes an objective function,

and/or is feasible.• We can represent the solution space for the

problem using a state space tree– The root of the tree represents 0 choices, – Nodes at depth 1 represent first choice – Nodes at depth 2 represent the second choice,

etc. – In this tree a path from a root to a leaf

represents a candidate solution

Backtracking 4

Sum of subsets

• Problem: Given n positive integers w1, ... wn and a

positive integer S. Find all subsets of w1, ... wn

that sum to S. • Example:

n=3, S=6, and w1=2, w2=4, w3=6

• Solutions: {2,4} and {6}

Backtracking 5

Sum of subsets

• We will assume a binary state space tree.

• The nodes at depth 1 are for including (yes, no) item 1, the nodes at depth 2 are for item 2, etc.

• The left branch includes wi, and the right branch excludes wi.

• The nodes contain the sum of the weights included so far

Backtracking 6

Sum of subset Problem: State SpaceTree for 3 items

w1 = 2, w2 = 4, w3 = 6 and S = 6

i1

i2

i3

yes no

0

0

0

0

2

2

2

6

612 8

4

410 6

yes

yes

no

no

no

nonono

The sum of the included integers is stored at the node.

yes

yes yesyes

Backtracking 7

A Depth First Search solution

• Problems can be solved using depth first search of the (implicit) state space tree.

• Each node will save its depth and its (possibly partial) current solution

• DFS can check whether node v is a leaf. – If it is a leaf then check if the current solution

satisfies the constraints– Code can be added to find the optimal solution

Backtracking 8

A DFS solution

• Such a DFS algorithm will be very slow.

• It does not check for every solution state (node) whether a solution has been reached, or whether a partial solution can lead to a feasible solution

• Is there a more efficient solution?

Backtracking 9

Backtracking

• Definition: We call a node nonpromising if it cannot lead to a feasible (or optimal) solution, otherwise it is promising

• Main idea: Backtracking consists of doing a DFS of the state space tree, checking whether each node is promising and if the node is nonpromising backtracking to the node’s parent

Backtracking 10

Backtracking

• The state space tree consisting of expanded nodes only is called the pruned state space tree

• The following slide shows the pruned state space tree for the sum of subsets example

• There are only 15 nodes in the pruned state space tree

• The full state space tree has 31 nodes

Backtracking 11

A Pruned State Space Tree (find all solutions)w1 = 3, w2 = 4, w3 = 5, w4 = 6; S = 13

0

0

03

3

3

7

712 8

4

49

5

3

4 40

0

0

5 50 0 0

06

13 7

Sum of subsets problem

Backtracking 12

Backtracking algorithm

void checknode (node v) {node u

if (promising ( v ))if (aSolutionAt( v ))

write the solutionelse //expand the node for ( each child u of v )

checknode ( u )

Backtracking 13

Checknode

• Checknode uses the functions:

– promising(v) which checks that the partial solution represented by v can lead to the required solution

– aSolutionAt(v) which checks whether the partial solution represented by node v solves the problem.

Backtracking 14

Sum of subsets – when is a node “promising”?

• Consider a node at depth i• weightSoFar = weight of node, i.e., sum of numbers

included in partial solution node represents

• totalPossibleLeft = weight of the remaining items i+1 to n (for a node at depth i)

• A node at depth i is non-promising if (weightSoFar + totalPossibleLeft < S ) or (weightSoFar + w[i+1] > S )

• To be able to use this “promising function” the wi must be sorted in non-decreasing order

Backtracking 15

A Pruned State Space Treew1 = 3, w2 = 4, w3 = 5, w4 = 6; S = 13

0

0

03

3

3

7

712 8

4

49

5

3

4 40

0

0

5 50 0 0

06

13 7 - backtrack

1

2

3

4 5

6 7

8

109

11

12 15

1413

Nodes numbered in “call” order

Backtracking 16

sumOfSubsets ( i, weightSoFar, totalPossibleLeft )

1) if (promising ( i )) //may lead to solution

2) then if ( weightSoFar == S )3) then print include[ 1 ] to include[ i ] //found solution4) else //expand the node when weightSoFar < S5) include [ i + 1 ] = "yes” //try including6) sumOfSubsets ( i + 1,

weightSoFar + w[i + 1],totalPossibleLeft - w[i + 1] )

7) include [ i + 1 ] = "no” //try excluding8) sumOfSubsets ( i + 1, weightSoFar ,

totalPossibleLeft - w[i + 1] )

boolean promising (i )1) return ( weightSoFar + totalPossibleLeft S) &&

( weightSoFar == S || weightSoFar + w[i + 1] S )

Prints all solutions!

Initial call sumOfSubsets(0, 0, )

n

iiw

1

Backtracking 17

Backtracking for optimization problems

• To deal with optimization we compute:best - value of best solution achieved so farvalue(v) - the value of the solution at node v

– Modify promising(v)

• Best is initialized to a value that is equal to a candidate solution or worse than any possible solution.

• Best is updated to value(v) if the solution at v is “better”

• By “better” we mean:– larger in the case of maximization and – smaller in the case of minimization

Backtracking 18

Modifying promising

• A node is promising when – it is feasible and can lead to a feasible solution

and– “there is a chance that a better solution than

best can be achieved by expanding it”• Otherwise it is nonpromising

• A bound on the best solution that can be achieved by expanding the node is computed and compared to best

• If the bound > best for maximization, (< best for minimization) the node is promising

How isit determined?

Backtracking 19

Modifying promising for Maximization Problems

• For a maximization problem the bound is an upper bound, – the largest possible solution that can be

achieved by expanding the node is less or equal to the upper bound

• If upper bound > best so far, a better solution may be found by expanding the node and the feasible node is promising

Backtracking 20

Modifying promising for Minimization Problems

• For minimization the bound is a lower bound, – the smallest possible solution that can be

achieved by expanding the node is less or equal to the lower bound

• If lower bound < best a better solution may be found and the feasible node is promising

Backtracking 21

Template for backtracking in the case of optimization problems.

Procedure checknode (node v ) {

node u ;

if ( value(v) is better than best )best = value(v);

if (promising (v) )for (each child u of v)

checknode (u );

}

• best is the best value so far and is initialized to a value that is equal or worse than any possible solution.

• value(v) is the value of the solution at the node.

Backtracking 22

Notation for knapsack

• We use maxprofit to denote best• profit(v) to denote value(v)

Backtracking 23

The state space tree for knapsack

• Each node v will include 3 values: – profit (v) = sum of profits of all items included in

the knapsack (on a path from root to v)– weight (v)= the sum of the weights of all items

included in the knapsack (on a path from root to v)– upperBound(v). upperBound(v) is greater or equal

to the maximum benefit that can be found by expanding the whole subtree of the state space tree with root v.

• The nodes are numbered in the order of expansion

Backtracking 24

Promising nodes for 0/1 knapsack

• Node v is promising if weight(v) < C, and upperBound(v)>maxprofit

• Otherwise it is not promising• Note that when weight(v) = C, or maxprofit =

upperbound(v) the node is non promising

Backtracking 25

Main idea for upper bound

• Theorem: The optimal profit for 0/1 knapsack

optimal profit for KWF

• Proof:

• Clearly the optimal solution to 0/1 knapsack is a

possible solution to KWF. So the optimal profit of KWF

is greater or equal to that of 0/1 knapsack

• Main idea: KWF can be used for computing the upper

bounds

Backtracking 26

Computing the upper bound for 0/1 knapsack

• Given node v at depth i.

• UpperBound(v) =

KWF2(i+1, weight(v), profit(v), w, p, C, n) • KWF2 requires that the items be ordered by non

increasing pi / wi, so if we arrange the items in this order before applying the backtracking algorithm, KWF2 will pick the remaining items in the required order.

Backtracking 27

KWF2(i, weight, profit, w, p, C, n)

1. bound = profit2. for j=i to n3. x[j]=0 //initialize variables to 04. while (weight<C)&& (i<=n) //not “full”and more items5. if weight+w[i]<=C //room for next item6. x[i]=1 //item i is added to knapsack7. weight=weight+w[i]; bound = bound +p[i]8. else9. x[i]=(C-weight)/w[i] //fraction of i added to knapsack10. weight=C; bound = bound + p[i]*x[i]11. i=i+1 // next item12. return bound

• KWF2 is in O(n) (assuming items sorted before applying backtracking)

Backtracking 28

C++ version

• The arrays w, p, include and bestset have size n+1.

• Location 0 is not used• include contains the current solution• bestset the best solution so far

Backtracking 29

Before calling Knapsack

numbest=0; //number of items considered maxprofit=0;knapsack(0,0,0);cout << maxprofit;for (i=1; i<= numbest; i++)

cout << bestset[i]; //the best solution

• maxprofit is initialized to $0, which is the worst profit that can be achieved with positive pis

• In Knapsack - before determining if node v is promising, maxprofit and bestset are updated

Backtracking 30

knapsack(i, profit, weight)

if ( weight <= C && profit > maxprofit)

// save better solution

maxprofit=profit //save new profit

numbest= i; bestset = include//save solution

if promising(i)

include [i + 1] = “ yes”

knapsack(i+1, profit+p[i+1], weight+ w[i+1])

include[i+1] = “no”

knapsack(i+1,profit,weight)

Backtracking 31

Promising(i)

promising(i)

//Cannot get a solution by expanding node

if weight >= C return false

//Compute upper bound

bound = KWF2(i+1, weight, profit, w, p, C, n)

return (bound>maxprofit)

Backtracking 32

Example from Neapolitan & Naimipour

• Suppose n = 4, W = 16, and we have the following:

i pi wi pi / wi

1 $40 2 $20

2 $30 5 $6

3 $50 10 $5

4 $10 5 $2• Note the the items are in the correct order needed by

KWF

Backtracking 33

maxprofit = $0 (n = 4, C = 16 )

Node 1a) profit = $ 0 weight = 0

b) bound = profit + p1 + p2 + (C - 7 ) * p3 / w3

= $0 + $40 + $30 + (16 -7) X $50/10 =$115

c) 1 is promising because its weight =0 < C = 16and its bound $115 > 0 the value of maxprofit.

The calculation for node 1

Backtracking 34

Item 1 with profit $40 and weight 2 is included

maxprofit = $40

a) profit = $40 weight = 2

b) bound = profit + p2 + (C - 7) X p3 / w3

= $40 + $30 + (16 -7) X $50/10 =$115

c) 2 is promising because its weight =2 < C = 16and its bound $115 > $40 the value of

maxprofit.

The calculation for node 2

Backtracking 35

Item 1 with profit $40 and weight 2 is not included

At this point maxprofit=$90 and is not changed

a) profit = $0 weight = 0

b) bound = profit + p2 + p3+ (C - 15) X p4 / w4

= $0 + $30 +$50+ (16 -15) X $10/5 =$82

c) 13 is nonpromising because its bound $82 < $90 the value of maxprofit.

The calculation for node 13

Backtracking 36

1

13

$00$115

$402$115

$00$82

Item 1 [$40, 2]

B82<90Item 2 [$30, 5]

$707$115

$12017

2

3

4

F17>16

Item 3 [$50, 10]

Item 4 [$10, 5]

$402$98

8

$707$80

5$9012$98

9$402$50

12

B50<90

$8012$80

6$707$70

7

N N

$10017

10 $9012$90

11

maxprofit = 90

profitweightbound

ExampleF - not feasibleN - not optimalB- cannot lead to best solution

Optimal

maxprofit =0

maxprofit =40

maxprofit =70

maxprofit =80F17>16